Multi agent system for negotiation in supply chain management

Supply chain management (SCM) is an emerging field that has commanded attention and support from the industrial community. Supply chain (SC) is defined as the chain linking each entity of the manufacturing and supply process from raw materials through to the end user. In order to increase supply chain effectiveness, minimize total cost, and reduce the bullwhip effect, integration and coordination of different systems and processes in the supply chain are required using information technology and effective communication and negotiation mechanism. To solve this problem, Agent technology provides the distributed environment a great promise of effective communication. The agent technology facilitates the integration of the entire supply chain as a networked system of independent echelon. In this article, a multi agent system has been developed to simulate a multi echelon supply chain. Each entity is modeled as one agent and their coordination lead to control inventories and minimize the total cost of SC by sharing information and forecasting knowledge and using negotiation mechanism. The result showed a reasonable reduction in total cost and bullwhip effect

An agent-based dynamic information network for supply chain management

One of the main research issues in supply chain management is to improve the global efficiency of supply chains. However, the improvement efforts often fail because supply chains are complex, are subject to frequent changes, and collaboration and information sharing in the supply chains are often infeasible. This paper presents a practical collaboration framework for supply chain management wherein multi-agent systems form dynamic information networks and coordinate their production and order planning according to synchronized estimation of market demands. In the framework, agents employ an iterative relaxation contract net protocol to find the most desirable suppliers by using data envelopment analysis. Furthermore, the chain of buyers and suppliers, from the end markets to raw material suppliers, form dynamic information networks for synchronized planning. This paper presents an agent-based dynamic information network for supply chain management and discusses the associated pros and cons

Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems

As the business environment gets more complicated, organizations must be able to respond to the business changes and adjust themselves quickly to gain their competitive advantages. This study proposes an integrated agent system, called SPA, which coordinates simulated and physical agents to provide an efficient way for organizations to meet the challenges in managing supply chains. In the integrated framework, physical agents coordinate with inter-organizations\' physical agents to form workable business processes and detect the variations occurring in the outside world, whereas simulated agents model and analyze the what-if scenarios to support physical agents in making decisions. This study uses a supply chain that produces digital still cameras as an example to demonstrate how the SPA works. In this example, individual information systems of the involved companies equip with the SPA and the entire supply chain is modeled as a hierarchical object oriented Petri nets. The SPA here applies the modified AGNES data clustering technique and the moving average approach to help each firm generalize customers\' past demand patterns and forecast their future demands. The amplitude of forecasting errors caused by bullwhip effects is used as a metric to evaluate the degree that the SPA affects the supply chain performance. The experimental results show that the SPA benefits the entire supply chain by reducing the bullwhip effects and forecasting errors in a dynamic environment.Supply Chain Performance Enhancement; Bullwhip Effects; Simulated Agents; Physical Agents; Dynamic Customer Demand Pattern Discovery

Supply chain business modelling

The developed work is motivated by the hypothesis that the presented Supply Chain Business Model is a practical and comprehensive approach to support not only operational day-to-day business decisions, but most importantly strategic and long term decisions that may define the success and the longevity of a business. Conceptually, the Business Supply Chain Model developed in this thesis replicates the behaviour and decision making of the different agents in a supply chain, and an Optimisation Module determines the optimised parameters that maximise the overall business profit, whatever scenario it may be. In the optimisation module, a Genetic Algorithm was used to determine the best equation parameters for each individual agent that optimise the overall supply chain profit. Furthermore, several business case-scenarios are presented and the findings highlighted. These case-scenarios prove that: the HC model is robust when subjected to predictable or unpredictable causes of variability; the bullwhip effect can be reduced significantly by applying GA as the optimisation tool; the improvement of profits needs to be evaluated at a global scale, independently of the individual agents’ profit; impact of supply shortages in the SC ; retail expansion analysis; delivery patterns change impact in profitability; impact of sourcing decisions in the SC profitability; model suitability for seasonal vs. non-seasonal products. The SC Modelling framework generic and globalising approach means that is easily applied and transposed to any other business realities and it can be easily changed to reflect other SC scenarios. The costing model associated means that, at any point in the network, all costs and profits can be easily measured. For the first time the shelf-life of a product captured and losses of product due to BBE dates, quantified. In this model the optimisation methodology runs parallel to the developed simulation tool, so the optimisation should be only run for new scenarios

Development of a framework for the integration of real-time data in supply chain risk management using simulation tools

Outgoin

Integration of lean six sigma with multi agent systems in the food distribution industry in small to medium enterprises

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe service industry worldwide continues to face unprecedented challenges in decision-making and in managing the operations involved in delivering products at low cost and ever-faster delivery speeds. These pressures exert an even greater impact upon small- and medium-sized enterprises (SMEs) involved in this industry who, influenced by globalisation, have to respond by handling the dynamic complexity within their operational supply chain. Many larger firms have implemented Lean and Six Sigma (LSS) and end-to-end integrated real-time information systems (RTI) that provide the information and the mechanisms needed to support flexibility and prompt decision-making. The recent emergence of new technologies such as multi-agent systems (MAS) provides enhanced capability to address complexity and decision-making with greater ease of use at a reduced cost. Whilst the application of Lean and Six Sigma are supported by significant published research, the application of integrated LSS and MAS in food distribution, especially in SMEs, is not. This study seeks to provide research to address this shortcoming for SMEs within the food distribution sector within Saudi Arabia, how this integrated approach can offer considerable performance improvement in SMEs and provide a base for further contributions in this field. This research undertook an empirical case study in Saudi Arabia to test the application of LSS in a food distribution SME. This approach demonstrated a significant improvement in the Six Sigma for late delivery. A single-stage MAS application extended this improvement, demonstrating that there is value in its application. The study conducted a survey of 39 firms in this sector to gain an insight into their current practices and challenges. The findings indicated there was a lack of Lean and Six Sigma principles adopted and that a lack of use of interconnected real-time systems to support decision-making and complex operational SCs. These findings identified the opportunity to design a conceptual framework with a stepped approach that integrated LSS with MAS, which was then developed on a Java-Assisted DEvelopment Framework (JADE) platform and tested using real-world data in an SME empirical case study. The results of the sequence of applications and the final simulations proved that this integrated Lean multi-agent system (LMAS) solution offered such substantial improvements in quality, time and costs that the SME considered that those factors justified making its implementation a priority

Applying multi-agent technology to supply chain management

Supply Chain Management (SCM) is extremely important to the manufacturing and retailing industries. Profitability of enterprises in these two industries depends on the efficiency and effectiveness on they manage their supply chains. Information technology has been a key element in supporting supply chain management. As the access to the Internet becomes easier, managers are able to access real-time data and use decision support tools to support their decision making. In this paper, we investigate how multi-agent technology and constraint network can be integrated together to improve the efficiency and transparency of supply chain management. A multi-agent based supply chain management system has been developed to support communication, coordination, collaboration, and operation of different entities in supply chains. Which enabled the warehouses and plants to query and share information. A constraint network model has been applied to model the objectives and constraints of each entity in supply chains. Two experiments have been conducted to evaluate the usefulness and performance of the system with participation of a retailing firm. The results indicated that the proposed system has several benefits. For example, improve efficiency by saving time and efforts for decision makers as well as improve the quality and responsiveness of decision making by value-added services, such as, bullwhip detection agent.